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Network Identification with an application to the identification of metabolic pathways

Subject: Network Identification with an application to the identification of metabolic pathways
Staff Mentor: X.J.A. Bombois
Other Mentor(s): A. Dankers, A Abate, P. Heuberger and P. Van den Hof
Keywords: System identification and estimation
Description: In many areas of science, researchers are interested in discovering the interconnection structure between measured variables. In biology, the use of quantitative methods to discover the structures of systems has revolutionized the field and led to many new discoveries, which is epitomized by the growth of a new area called systems biology. In system identification there has been relatively little activity in the area of identifying networks with complex interconnection structures. Most of the attention has been directed towards the simple open and closed loop structures. Recently, we have proposed some methods to identify the dynamics of a network. Both of these algorithms show great potential to identify not only the dynamics but also the interconnection structure of a network.

In this project these algorithms should be adapted so that both the dynamics and the interconnection structure of the network can be identified. The methods will then be used to identify the metabolic pathway of an organism (i.e. identify the mechanisms by which cells convert nutrient into products). The data set for the test system consists the measured concentrations of various metabolites making up the metabolic pathway (Abate, Hillen, and Wahl 2012). The structure of the network in this case is known to be that as shown in the figure below, and the methods developed in this project should be able to recover this structure. If the proposed methods work well on the test system, they will be applied to experimental data and to systems where the structure of the network is not known beforehand.


Abate, A., R.C. Hillen, and S.A. Wahl (2012). “Piecewise Affine Approximation of fluxes and enzyme kinetics from in-vivo 13C labeling experiments”. In: International Journal of Robust and Nonlinear Control. Special Issue on System Identification for Biological Systems – doi:10.1002/rnc.2798.

Network considered in this research

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Last modified: 13 April 2012, 11:25 UTC
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